Source code for ray.air.integrations.wandb

import enum
import os
import pickle
import urllib
import warnings

import numpy as np
from numbers import Number

from types import ModuleType
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union

import ray
from ray import logger
from ray.air import session

from ray.tune.logger import LoggerCallback
from ray.tune.utils import flatten_dict
from ray.tune.experiment import Trial

from ray._private.storage import _load_class
from ray.tune.utils.node import _force_on_current_node
from ray.util import PublicAPI
from ray.util.queue import Queue

try:
    import wandb
    from wandb.util import json_dumps_safer
    from wandb.wandb_run import Run
    from wandb.sdk.lib.disabled import RunDisabled
    from wandb.sdk.data_types.base_types.wb_value import WBValue
except ImportError:
    wandb = json_dumps_safer = Run = RunDisabled = WBValue = None


WANDB_ENV_VAR = "WANDB_API_KEY"
WANDB_PROJECT_ENV_VAR = "WANDB_PROJECT_NAME"
WANDB_GROUP_ENV_VAR = "WANDB_GROUP_NAME"
WANDB_MODE_ENV_VAR = "WANDB_MODE"
# Hook that is invoked before wandb.init in the setup method of WandbLoggerCallback
# to populate the API key if it isn't already set when initializing the callback.
# It doesn't take in any arguments and returns the W&B API key.
# Example: "your.module.wandb_setup_api_key_hook".
WANDB_SETUP_API_KEY_HOOK = "WANDB_SETUP_API_KEY_HOOK"
# Hook that is invoked before wandb.init in the setup method of WandbLoggerCallback
# to populate environment variables to specify the location
# (project and group) of the W&B run.
# It doesn't take in any arguments and doesn't return anything, but it does populate
# WANDB_PROJECT_NAME and WANDB_GROUP_NAME.
# Example: "your.module.wandb_populate_run_location_hook".
WANDB_POPULATE_RUN_LOCATION_HOOK = "WANDB_POPULATE_RUN_LOCATION_HOOK"
# Hook that is invoked after running wandb.init in WandbLoggerCallback
# to process information about the W&B run.
# It takes in a W&B run object and doesn't return anything.
# Example: "your.module.wandb_process_run_info_hook".
WANDB_PROCESS_RUN_INFO_HOOK = "WANDB_PROCESS_RUN_INFO_HOOK"


[docs]@PublicAPI(stability="alpha") def setup_wandb( config: Optional[Dict] = None, api_key: Optional[str] = None, api_key_file: Optional[str] = None, rank_zero_only: bool = True, **kwargs, ) -> Union[Run, RunDisabled]: """Set up a Weights & Biases session. This function can be used to initialize a Weights & Biases session in a (distributed) training or tuning run. By default, the run ID is the trial ID, the run name is the trial name, and the run group is the experiment name. These settings can be overwritten by passing the respective arguments as ``kwargs``, which will be passed to ``wandb.init()``. In distributed training with Ray Train, only the zero-rank worker will initialize wandb. All other workers will return a disabled run object, so that logging is not duplicated in a distributed run. This can be disabled by passing ``rank_zero_only=False``, which will then initialize wandb in every training worker. The ``config`` argument will be passed to Weights and Biases and will be logged as the run configuration. If no API key or key file are passed, wandb will try to authenticate using locally stored credentials, created for instance by running ``wandb login``. Keyword arguments passed to ``setup_wandb()`` will be passed to ``wandb.init()`` and take precedence over any potential default settings. Args: config: Configuration dict to be logged to Weights and Biases. Can contain arguments for ``wandb.init()`` as well as authentication information. api_key: API key to use for authentication with Weights and Biases. api_key_file: File pointing to API key for with Weights and Biases. rank_zero_only: If True, will return an initialized session only for the rank 0 worker in distributed training. If False, will initialize a session for all workers. kwargs: Passed to ``wandb.init()``. Example: .. code-block: python from ray.air.integrations.wandb import wandb_setup def training_loop(config): wandb = wandb_setup(config) # ... wandb.log({"loss": 0.123}) """ if not wandb: raise RuntimeError( "Wandb was not found - please install with `pip install wandb`" ) try: # Do a try-catch here if we are not in a train session _session = session._get_session(warn=False) if _session and rank_zero_only and session.get_world_rank() != 0: return RunDisabled() default_trial_id = session.get_trial_id() default_trial_name = session.get_trial_name() default_experiment_name = session.get_experiment_name() except RuntimeError: default_trial_id = None default_trial_name = None default_experiment_name = None # Default init kwargs wandb_init_kwargs = { "trial_id": kwargs.get("trial_id") or default_trial_id, "trial_name": kwargs.get("trial_name") or default_trial_name, "group": kwargs.get("group") or default_experiment_name, } # Passed kwargs take precedence over default kwargs wandb_init_kwargs.update(kwargs) return _setup_wandb( config=config, api_key=api_key, api_key_file=api_key_file, **wandb_init_kwargs )
def _setup_wandb( trial_id: str, trial_name: str, config: Optional[Dict] = None, api_key: Optional[str] = None, api_key_file: Optional[str] = None, _wandb: Optional[ModuleType] = None, **kwargs, ) -> Union[Run, RunDisabled]: _config = config.copy() if config else {} wandb_config = _config.pop("wandb", {}).copy() # Deprecate: 2.4 if wandb_config: warnings.warn( "Passing a `wandb` key in the config dict is deprecated and will raise an " "error in the future. Please pass the actual arguments to `setup_wandb()` " "instead.", DeprecationWarning, ) # If key file is specified, set api_key_file = api_key_file or wandb_config.pop("api_key_file", None) if api_key_file: api_key_file = os.path.expanduser(api_key_file) _set_api_key(api_key_file, api_key or wandb_config.pop("api_key", None)) wandb_config["project"] = _get_wandb_project(wandb_config.get("project")) wandb_config["group"] = ( os.environ.get(WANDB_GROUP_ENV_VAR) if (not wandb_config.get("group") and os.environ.get(WANDB_GROUP_ENV_VAR)) else wandb_config.get("group") ) # remove unpickleable items _config = _clean_log(_config) wandb_init_kwargs = dict( id=trial_id, name=trial_name, resume=True, reinit=True, allow_val_change=True, config=_config, ) # Update config (e.g.g set group, project, override other settings) wandb_init_kwargs.update(wandb_config) wandb_init_kwargs.update(**kwargs) # On windows, we can't fork if os.name == "nt": os.environ["WANDB_START_METHOD"] = "thread" else: os.environ["WANDB_START_METHOD"] = "fork" _wandb = _wandb or wandb run = _wandb.init(**wandb_init_kwargs) _run_wandb_process_run_info_hook(run) return run def _is_allowed_type(obj): """Return True if type is allowed for logging to wandb""" if isinstance(obj, np.ndarray) and obj.size == 1: return isinstance(obj.item(), Number) if isinstance(obj, Sequence) and len(obj) > 0: return isinstance(obj[0], WBValue) return isinstance(obj, (Number, WBValue)) def _clean_log(obj: Any): # Fixes https://github.com/ray-project/ray/issues/10631 if isinstance(obj, dict): return {k: _clean_log(v) for k, v in obj.items()} elif isinstance(obj, (list, set)): return [_clean_log(v) for v in obj] elif isinstance(obj, tuple): return tuple(_clean_log(v) for v in obj) elif _is_allowed_type(obj): return obj # Else try: # This is what wandb uses internally. If we cannot dump # an object using this method, wandb will raise an exception. json_dumps_safer(obj) # This is probably unnecessary, but left here to be extra sure. pickle.dumps(obj) return obj except Exception: # give up, similar to _SafeFallBackEncoder fallback = str(obj) # Try to convert to int try: fallback = int(fallback) return fallback except ValueError: pass # Try to convert to float try: fallback = float(fallback) return fallback except ValueError: pass # Else, return string return fallback def _get_wandb_project(project: Optional[str] = None) -> Optional[str]: """Get W&B project from environment variable or external hook if not passed as and argument.""" if ( not project and not os.environ.get(WANDB_PROJECT_ENV_VAR) and os.environ.get(WANDB_POPULATE_RUN_LOCATION_HOOK) ): # Try to populate WANDB_PROJECT_ENV_VAR and WANDB_GROUP_ENV_VAR # from external hook try: _load_class(os.environ[WANDB_POPULATE_RUN_LOCATION_HOOK])() except Exception as e: logger.exception( f"Error executing {WANDB_POPULATE_RUN_LOCATION_HOOK} to " f"populate {WANDB_PROJECT_ENV_VAR} and {WANDB_GROUP_ENV_VAR}: {e}", exc_info=e, ) if not project and os.environ.get(WANDB_PROJECT_ENV_VAR): # Try to get project and group from environment variables if not # passed through WandbLoggerCallback. project = os.environ.get(WANDB_PROJECT_ENV_VAR) return project def _set_api_key(api_key_file: Optional[str] = None, api_key: Optional[str] = None): """Set WandB API key from `wandb_config`. Will pop the `api_key_file` and `api_key` keys from `wandb_config` parameter. The order of fetching the API key is: 1) From `api_key` or `api_key_file` arguments 2) From WANDB_API_KEY environment variables 3) User already logged in to W&B (wandb.api.api_key set) 4) From external hook WANDB_SETUP_API_KEY_HOOK """ if os.environ.get(WANDB_MODE_ENV_VAR) in {"offline", "disabled"}: return if api_key_file: if api_key: raise ValueError("Both WandB `api_key_file` and `api_key` set.") with open(api_key_file, "rt") as fp: api_key = fp.readline().strip() if not api_key and not os.environ.get(WANDB_ENV_VAR): # Check if user is already logged into wandb. try: wandb.ensure_configured() if wandb.api.api_key: logger.info("Already logged into W&B.") return except AttributeError: pass # Try to get API key from external hook if WANDB_SETUP_API_KEY_HOOK in os.environ: try: api_key = _load_class(os.environ[WANDB_SETUP_API_KEY_HOOK])() except Exception as e: logger.exception( f"Error executing {WANDB_SETUP_API_KEY_HOOK} to setup API key: {e}", exc_info=e, ) if api_key: os.environ[WANDB_ENV_VAR] = api_key elif not os.environ.get(WANDB_ENV_VAR): raise ValueError( "No WandB API key found. Either set the {} environment " "variable, pass `api_key` or `api_key_file` to the" "`WandbLoggerCallback` class as arguments, " "or run `wandb login` from the command line".format(WANDB_ENV_VAR) ) def _run_wandb_process_run_info_hook(run: Any) -> None: """Run external hook to process information about wandb run""" if WANDB_PROCESS_RUN_INFO_HOOK in os.environ: try: _load_class(os.environ[WANDB_PROCESS_RUN_INFO_HOOK])(run) except Exception as e: logger.exception( f"Error calling {WANDB_PROCESS_RUN_INFO_HOOK}: {e}", exc_info=e ) class _QueueItem(enum.Enum): END = enum.auto() RESULT = enum.auto() CHECKPOINT = enum.auto() class _WandbLoggingActor: """ Wandb assumes that each trial's information should be logged from a separate process. We use Ray actors as forking multiprocessing processes is not supported by Ray and spawn processes run into pickling problems. We use a queue for the driver to communicate with the logging process. The queue accepts the following items: - If it's a dict, it is assumed to be a result and will be logged using ``wandb.log()`` - If it's a checkpoint object, it will be saved using ``wandb.log_artifact()``. """ def __init__( self, logdir: str, queue: Queue, exclude: List[str], to_config: List[str], *args, **kwargs, ): import wandb self._wandb = wandb os.chdir(logdir) self.queue = queue self._exclude = set(exclude) self._to_config = set(to_config) self.args = args self.kwargs = kwargs self._trial_name = self.kwargs.get("name", "unknown") self._logdir = logdir def run(self): # Since we're running in a separate process already, use threads. os.environ["WANDB_START_METHOD"] = "thread" run = self._wandb.init(*self.args, **self.kwargs) run.config.trial_log_path = self._logdir _run_wandb_process_run_info_hook(run) while True: item_type, item_content = self.queue.get() if item_type == _QueueItem.END: break if item_type == _QueueItem.CHECKPOINT: self._handle_checkpoint(item_content) continue assert item_type == _QueueItem.RESULT log, config_update = self._handle_result(item_content) try: self._wandb.config.update(config_update, allow_val_change=True) self._wandb.log(log) except urllib.error.HTTPError as e: # Ignore HTTPError. Missing a few data points is not a # big issue, as long as things eventually recover. logger.warn("Failed to log result to w&b: {}".format(str(e))) self._wandb.finish() def _handle_checkpoint(self, checkpoint_path: str): artifact = self._wandb.Artifact( name=f"checkpoint_{self._trial_name}", type="model" ) artifact.add_dir(checkpoint_path) self._wandb.log_artifact(artifact) def _handle_result(self, result: Dict) -> Tuple[Dict, Dict]: config_update = result.get("config", {}).copy() log = {} flat_result = flatten_dict(result, delimiter="/") for k, v in flat_result.items(): if any(k.startswith(item + "/") or k == item for item in self._exclude): continue elif any(k.startswith(item + "/") or k == item for item in self._to_config): config_update[k] = v elif not _is_allowed_type(v): continue else: log[k] = v config_update.pop("callbacks", None) # Remove callbacks return log, config_update
[docs]class WandbLoggerCallback(LoggerCallback): """WandbLoggerCallback Weights and biases (https://www.wandb.ai/) is a tool for experiment tracking, model optimization, and dataset versioning. This Ray Tune ``LoggerCallback`` sends metrics to Wandb for automatic tracking and visualization. Example: .. testcode:: import random from ray import tune from ray.air import session, RunConfig from ray.air.integrations.wandb import WandbLoggerCallback def train_func(config): offset = random.random() / 5 for epoch in range(2, config["epochs"]): acc = 1 - (2 + config["lr"]) ** -epoch - random.random() / epoch - offset loss = (2 + config["lr"]) ** -epoch + random.random() / epoch + offset session.report({"acc": acc, "loss": loss}) tuner = tune.Tuner( train_func, param_space={ "lr": tune.grid_search([0.001, 0.01, 0.1, 1.0]), "epochs": 10, }, run_config=RunConfig( callbacks=[WandbLoggerCallback(project="Optimization_Project")] ), ) results = tuner.fit() .. testoutput:: :hide: :options: +ELLIPSIS ... Args: project: Name of the Wandb project. Mandatory. group: Name of the Wandb group. Defaults to the trainable name. api_key_file: Path to file containing the Wandb API KEY. This file only needs to be present on the node running the Tune script if using the WandbLogger. api_key: Wandb API Key. Alternative to setting ``api_key_file``. excludes: List of metrics and config that should be excluded from the log. log_config: Boolean indicating if the ``config`` parameter of the ``results`` dict should be logged. This makes sense if parameters will change during training, e.g. with PopulationBasedTraining. Defaults to False. upload_checkpoints: If ``True``, model checkpoints will be uploaded to Wandb as artifacts. Defaults to ``False``. **kwargs: The keyword arguments will be pased to ``wandb.init()``. Wandb's ``group``, ``run_id`` and ``run_name`` are automatically selected by Tune, but can be overwritten by filling out the respective configuration values. Please see here for all other valid configuration settings: https://docs.wandb.ai/library/init """ # noqa: E501 # Do not log these result keys _exclude_results = ["done", "should_checkpoint"] AUTO_CONFIG_KEYS = [ "trial_id", "experiment_tag", "node_ip", "experiment_id", "hostname", "pid", "date", ] """Results that are saved with `wandb.config` instead of `wandb.log`.""" _logger_actor_cls = _WandbLoggingActor def __init__( self, project: Optional[str] = None, group: Optional[str] = None, api_key_file: Optional[str] = None, api_key: Optional[str] = None, excludes: Optional[List[str]] = None, log_config: bool = False, upload_checkpoints: bool = False, save_checkpoints: bool = False, **kwargs, ): if not wandb: raise RuntimeError( "Wandb was not found - please install with `pip install wandb`" ) if save_checkpoints: warnings.warn( "`save_checkpoints` is deprecated. Use `upload_checkpoints` instead.", DeprecationWarning, ) upload_checkpoints = save_checkpoints self.project = project self.group = group self.api_key_path = api_key_file self.api_key = api_key self.excludes = excludes or [] self.log_config = log_config self.upload_checkpoints = upload_checkpoints self.kwargs = kwargs self._remote_logger_class = None self._trial_logging_actors: Dict[ "Trial", ray.actor.ActorHandle[_WandbLoggingActor] ] = {} self._trial_logging_futures: Dict["Trial", ray.ObjectRef] = {} self._trial_queues: Dict["Trial", Queue] = {} def setup(self, *args, **kwargs): self.api_key_file = ( os.path.expanduser(self.api_key_path) if self.api_key_path else None ) _set_api_key(self.api_key_file, self.api_key) self.project = _get_wandb_project(self.project) if not self.project: raise ValueError( "Please pass the project name as argument or through " f"the {WANDB_PROJECT_ENV_VAR} environment variable." ) if not self.group and os.environ.get(WANDB_GROUP_ENV_VAR): self.group = os.environ.get(WANDB_GROUP_ENV_VAR) def log_trial_start(self, trial: "Trial"): config = trial.config.copy() config.pop("callbacks", None) # Remove callbacks exclude_results = self._exclude_results.copy() # Additional excludes exclude_results += self.excludes # Log config keys on each result? if not self.log_config: exclude_results += ["config"] # Fill trial ID and name trial_id = trial.trial_id if trial else None trial_name = str(trial) if trial else None # Project name for Wandb wandb_project = self.project # Grouping wandb_group = self.group or trial.experiment_dir_name if trial else None # remove unpickleable items! config = _clean_log(config) config = { key: value for key, value in config.items() if key not in self.excludes } wandb_init_kwargs = dict( id=trial_id, name=trial_name, resume=False, reinit=True, allow_val_change=True, group=wandb_group, project=wandb_project, config=config, ) wandb_init_kwargs.update(self.kwargs) self._start_logging_actor(trial, exclude_results, **wandb_init_kwargs) def _start_logging_actor( self, trial: "Trial", exclude_results: List[str], **wandb_init_kwargs ): if not self._remote_logger_class: env_vars = {} # API key env variable is not set if authenticating through `wandb login` if WANDB_ENV_VAR in os.environ: env_vars[WANDB_ENV_VAR] = os.environ[WANDB_ENV_VAR] self._remote_logger_class = ray.remote( num_cpus=0, **_force_on_current_node(), runtime_env={"env_vars": env_vars}, )(self._logger_actor_cls) self._trial_queues[trial] = Queue( actor_options={"num_cpus": 0, **_force_on_current_node()} ) self._trial_logging_actors[trial] = self._remote_logger_class.remote( logdir=trial.logdir, queue=self._trial_queues[trial], exclude=exclude_results, to_config=self.AUTO_CONFIG_KEYS, **wandb_init_kwargs, ) self._trial_logging_futures[trial] = self._trial_logging_actors[ trial ].run.remote() def _stop_logging_actor(self, trial: "Trial", timeout: int = 10): self._trial_queues[trial].put((_QueueItem.END, None)) try: ray.get(self._trial_logging_futures[trial], timeout=timeout) except TimeoutError: ray.kill(self._trial_logging_actors[trial]) del self._trial_queues[trial] del self._trial_logging_actors[trial] del self._trial_logging_futures[trial] def log_trial_result(self, iteration: int, trial: "Trial", result: Dict): if trial not in self._trial_logging_actors: self.log_trial_start(trial) result = _clean_log(result) self._trial_queues[trial].put((_QueueItem.RESULT, result)) def log_trial_save(self, trial: "Trial"): if self.upload_checkpoints and trial.checkpoint: self._trial_queues[trial].put( (_QueueItem.CHECKPOINT, trial.checkpoint.dir_or_data) ) def log_trial_end(self, trial: "Trial", failed: bool = False): self._stop_logging_actor(trial=trial, timeout=10) def __del__(self): for trial in list(self._trial_logging_actors): self._stop_logging_actor(trial=trial, timeout=2) self._trial_logging_actors = {} self._trial_logging_futures = {} self._trial_queues = {}